Optimization
Last updated: 2023-08-03
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Knit directory: dgrp-starve/
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f <- readRDS("snake/data/zigure/lassoF.Rds")
m <- readRDS("snake/data/zigure/lassoM.Rds")
data <- f
gg[[1]] <- ggplot(data, aes(x=nfolds, y=cor, fill=relax)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm)
gg[[2]] <- ggplot(data, aes(x=nfolds, y=time, fill=relax)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm)
gg[[3]] <- ggplot(data, aes(x=dfmax, y=cor)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm)
gg[[4]] <- ggplot(data, aes(x=dfmax, y=time)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm)
data <- m
gg[[5]] <- ggplot(data, aes(x=nfolds, y=cor, fill=relax)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm)
gg[[6]] <- ggplot(data, aes(x=nfolds, y=time, fill=relax)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm)
gg[[7]] <- ggplot(data, aes(x=dfmax, y=cor)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm)
gg[[8]] <- ggplot(data, aes(x=dfmax, y=time)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm) Female Fold number vs. Correlation and Time
- relax decreases correlation while increasing runtime
plot_grid(gg[[1]],gg[[2]], ncol=2)
| Version | Author | Date |
|---|---|---|
| 29ae1fb | nklimko | 2023-08-03 |
dfMax
- No effect on correlation or runtime
plot_grid(gg[[3]],gg[[4]], ncol=2)
| Version | Author | Date |
|---|---|---|
| 29ae1fb | nklimko | 2023-08-03 |
Male Fold number vs. Correlation and Time
- relax decreases correlation while increasing runtime
plot_grid(gg[[5]],gg[[6]], ncol=2)
| Version | Author | Date |
|---|---|---|
| 29ae1fb | nklimko | 2023-08-03 |
dfMax
- No effect on correlation or runtime
plot_grid(gg[[7]],gg[[8]], ncol=2)
| Version | Author | Date |
|---|---|---|
| 29ae1fb | nklimko | 2023-08-03 |
Lasso Data
lassoFinal <- readRDS("snake/data/zigure/lassoFinal.Rds")
print(lassoFinal) method nfolds dfmax relax sex cor time
1: lasso 3 1000 0 f 0.4114329 1.58266
2: lasso 3 3000 0 f 0.4114329 1.58624
3: lasso 3 6000 0 f 0.4114329 1.59594
4: lasso 3 10000 0 f 0.4114329 1.61216
5: lasso 3 1000 1 f 0.3949298 5.31824
6: lasso 3 3000 1 f 0.3949298 5.33932
7: lasso 3 6000 1 f 0.3949298 5.32556
8: lasso 3 10000 1 f 0.3949298 5.29430
9: lasso 6 1000 0 f 0.4019169 2.86242
10: lasso 6 3000 0 f 0.4019169 2.89012
11: lasso 6 6000 0 f 0.4019169 2.88164
12: lasso 6 10000 0 f 0.4019169 2.84318
13: lasso 6 1000 1 f 0.3765922 10.39362
14: lasso 6 3000 1 f 0.3765922 10.40682
15: lasso 6 6000 1 f 0.3765922 10.33502
16: lasso 6 10000 1 f 0.3765922 10.35764
17: lasso 9 1000 0 f 0.3971659 3.40344
18: lasso 9 3000 0 f 0.3971659 3.39002
19: lasso 9 6000 0 f 0.3971659 3.43246
20: lasso 9 10000 0 f 0.3971659 3.45136
21: lasso 9 1000 1 f 0.3655954 15.47176
22: lasso 9 3000 1 f 0.3655954 15.72020
23: lasso 9 6000 1 f 0.3655954 15.48900
24: lasso 9 10000 1 f 0.3655954 15.45090
25: lasso 12 1000 0 f 0.3902660 4.03224
26: lasso 12 3000 0 f 0.3902660 3.99780
27: lasso 12 6000 0 f 0.3902660 3.97008
28: lasso 12 10000 0 f 0.3902660 3.97230
29: lasso 12 1000 1 f 0.3592104 21.19168
30: lasso 12 3000 1 f 0.3592104 21.14294
31: lasso 12 6000 1 f 0.3592104 21.10554
32: lasso 12 10000 1 f 0.3592104 21.12618
33: lasso 3 1000 0 m 0.3554582 1.77392
34: lasso 3 3000 0 m 0.3554582 1.76936
35: lasso 3 6000 0 m 0.3554582 1.80626
36: lasso 3 10000 0 m 0.3554582 1.78518
37: lasso 3 1000 1 m 0.3551354 5.81288
38: lasso 3 3000 1 m 0.3551354 5.80408
39: lasso 3 6000 1 m 0.3551354 5.79330
40: lasso 3 10000 1 m 0.3551354 5.79640
41: lasso 6 1000 0 m 0.3445264 3.36236
42: lasso 6 3000 0 m 0.3445264 3.37832
43: lasso 6 6000 0 m 0.3445264 3.34990
44: lasso 6 10000 0 m 0.3445264 3.37334
45: lasso 6 1000 1 m 0.3367717 11.48820
46: lasso 6 3000 1 m 0.3367717 11.57574
47: lasso 6 6000 1 m 0.3367717 11.50660
48: lasso 6 10000 1 m 0.3367717 11.55544
49: lasso 9 1000 0 m 0.3324251 4.03074
50: lasso 9 3000 0 m 0.3324251 4.12538
51: lasso 9 6000 0 m 0.3324251 4.04412
52: lasso 9 10000 0 m 0.3324251 4.05018
53: lasso 9 1000 1 m 0.3023836 17.68740
54: lasso 9 3000 1 m 0.3023836 17.64944
55: lasso 9 6000 1 m 0.3023836 17.70626
56: lasso 9 10000 1 m 0.3023836 17.77086
57: lasso 12 1000 0 m 0.3131776 4.72010
58: lasso 12 3000 0 m 0.3131776 4.69954
59: lasso 12 6000 0 m 0.3131776 4.76550
60: lasso 12 10000 0 m 0.3131776 4.78924
61: lasso 12 1000 1 m 0.2922959 23.88572
62: lasso 12 3000 1 m 0.2922959 23.85232
63: lasso 12 6000 1 m 0.2922959 23.82610
64: lasso 12 10000 1 m 0.2922959 23.89548
method nfolds dfmax relax sex cor timebayesFinal <- readRDS("snake/data/zigure/bayesFinal.Rds")
data <- bayesFinal
gg[[9]] <- ggplot(data, aes(x=R2, y=cor, fill=sex)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm)
gg[[10]] <- ggplot(data, aes(x=R2, y=time, fill=sex)) +
geom_point() +
geom_smooth(formula = y ~ x, method=lm) BayesC
plot_grid(gg[[9]],gg[[10]], ncol=2)
| Version | Author | Date |
|---|---|---|
| cfd86f2 | nklimko | 2023-08-03 |
print(bayesFinal) method R2 sex cor time
1: bayesC 0.1 f 0.2656316 437.8930
2: bayesC 0.5 f 0.3115005 430.0788
3: bayesC 0.9 f 0.3180012 439.2995
4: bayesC 0.1 m 0.3944273 496.1592
5: bayesC 0.5 m 0.4232572 515.0008
6: bayesC 0.9 m 0.4312989 529.1078Random Forest Female/Male
#0.364, 0.382 f m
data <- readRDS("snake/data/zigure/rf.Rds")
title <- "Correlation"
tagChar <- '.'
gg[[11]] <- ggplot(data, aes(x=sex, y=cor, fill=sex)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y=title,tag=tagChar) +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)
plot_grid(gg[[11]], ncol=1)
| Version | Author | Date |
|---|---|---|
| af8605c | nklimko | 2023-08-03 |
Data Driven Female/Male
#0.364, 0.382 f m
data <- readRDS("snake/data/zigure/dd.Rds")
title <- "Correlation"
tagChar <- '.'
gg[[12]] <- ggplot(data, aes(x=sex, y=cor, fill=sex)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y=title,tag=tagChar) +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)
plot_grid(gg[[12]], ncol=1)
| Version | Author | Date |
|---|---|---|
| 6eac9d1 | nklimko | 2023-08-03 |
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.5 (Green Obsidian)
Matrix products: default
BLAS/LAPACK: /opt/ohpc/pub/libs/gnu9/openblas/0.3.7/lib/libopenblasp-r0.3.7.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggcorrplot_0.1.4 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[5] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[9] tidyverse_2.0.0 scales_1.2.1 viridis_0.6.2 viridisLite_0.4.2
[13] qqman_0.1.8 cowplot_1.1.1 ggplot2_3.4.2 data.table_1.14.8
[17] dplyr_1.1.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.11 lattice_0.21-8 getPass_0.2-2 ps_1.7.5
[5] rprojroot_2.0.3 digest_0.6.33 utf8_1.2.3 R6_2.5.1
[9] evaluate_0.21 httr_1.4.5 highr_0.10 pillar_1.9.0
[13] rlang_1.1.1 rstudioapi_0.15.0 whisker_0.4.1 callr_3.7.3
[17] jquerylib_0.1.4 Matrix_1.6-0 rmarkdown_2.23 labeling_0.4.2
[21] splines_4.1.2 munsell_0.5.0 compiler_4.1.2 httpuv_1.6.9
[25] xfun_0.39 pkgconfig_2.0.3 mgcv_1.9-0 htmltools_0.5.5
[29] tidyselect_1.2.0 gridExtra_2.3 fansi_1.0.4 calibrate_1.7.7
[33] tzdb_0.3.0 withr_2.5.0 later_1.3.1 MASS_7.3-60
[37] grid_4.1.2 nlme_3.1-162 jsonlite_1.8.7 gtable_0.3.3
[41] lifecycle_1.0.3 git2r_0.31.0 magrittr_2.0.3 cli_3.6.1
[45] stringi_1.7.12 cachem_1.0.8 farver_2.1.1 fs_1.6.3
[49] promises_1.2.0.1 bslib_0.5.0 generics_0.1.3 vctrs_0.6.2
[53] tools_4.1.2 glue_1.6.2 hms_1.1.3 processx_3.8.2
[57] fastmap_1.1.1 yaml_2.3.7 timechange_0.2.0 colorspace_2.1-0
[61] knitr_1.43 sass_0.4.7